TBIO-07. Pediatric tumor classification through genome-wide methylation profiling of extracellular vesicle DNA

BACKGROUND Genome-wide methylation profiling reliably classifies pediatric central nervous system (CNS) tumors. Extracellular vesicles (EVs) are released by pediatric CNS tumor cells (pCC) and contain high molecular weight tumor DNA, rendering EVs a potential biomarker source to identify tumor subgroups, stratify patients and monitor therapy by liquid biopsy. We investigated whether the DNA in pCC-derived EVs reflects genome-wide tumor methylation profiles and allows tumor subtype classification. METHODS DNA was isolated from EVs secreted by pediatric CNS tumor cells (pCC) as well as from the shortly cultured tumor cells and from the original tumor samples (n=4 patients). Pediatric Fibroblasts and EVs derived thereof were used as a non-tumorous control. EVs were classified by nanoparticle analysis (NTA), immunoblotting, imaging flow cytometry (IFCM and electron microscopy. Genome-wide DNA methylation profiling was performed using an 850k Illumina EPIC array and results were classified according to the DKFZ brain tumor classifier and further analysed by t-SNE and Copy number alteration analysis (CNA). RESULTS The size range of pCC-derived EVs was 120–150 nm, as measured by NTA. The majority of secreted EVs exhibited high expression of common EV markers (i.e. CD9, CD63 and CD81), as characterized by IFCM. Genome-wide DNA methylation profiling of pCC-derived EVs correctly identified the methylation class of the original tumor (i.e. pilocytic astrocytoma, medulloblastoma). In addition, t-SNE analysis and copy number alterations matched the pattern of the parental pCC and original tumor samples. CONCLUSION EV DNA faithfully reflects the tumor methylation class and copy number alterations present in the parental cells and the original tumor. Methylation profiling of circulating tumor EV DNA could become a useful tool to detect and classify pediatric CNS tumors.

BACKGROUND Genome-wide methylation profiling has recently been developed into a tool that allows subtype tumor classification in central nervous system (CNS) tumors. Extracellular vesicles (EVs) are released by CNS tumor cells and contain high molecular weight tumor DNA, rendering EVs a potential biomarker source to identify tumor subgroups, stratify patients and monitor therapy by liquid biopsy. We investigated whether the DNA in glioma-derived EVs reflects genome-wide tumor methylation profiles and allows tumor subtype classification. METHODS DNA was isolated from EVs secreted by cultured glioma stem-like cells (GSC) as well as from the cells of origin and from the original tumor samples (n=3 patients). EVs were classified by nanoparticle analysis (NTA), immunoblotting, imaging flow cytometry (IFCM), multiplex EV assays and electron microscopy. Genome-wide DNA methylation profiling was performed using an 850k Illumina EPIC array and results were classified according to the DKFZ brain tumor classifier. RESULTS The size range of GSC-derived EVs was 120–150 nm, as measured by NTA. The majority of secreted EVs exhibited high expression of common EV markers (i.e. CD9, CD63 and CD81), as characterized by IFCM and multiplex EV assays. Genome-wide methylation profiling of GSC-derived EVs correctly identified the methylation class of the original tumor, including information on the IDH mutation status and subclass classification (RTK1, RTK2). In addition, copy number alterations and the MGMT metyhlation status matched the pattern of the parental GSCs and original tumor samples. CONCLUSION EV DNA faithfully reflects the tumor methylation class as well as the MGMT methylation status and copy number variations present in the parental cells and the original tumor. Methylation profiling of circulating tumor EV DNA could become a useful tool to detect and classify CNS tumors.

Genome-wide DNA methylation profiling has recently been developed into a tool that allows tumor classification in central nervous system tumors. Extracellular vesicles (EVs) are released by tumor cells and contain high molecular weight DNA, rendering EVs a potential biomarker source to identify tumor subgroups, stratify patients and monitor therapy by liquid biopsy. We investigated whether the DNA in glioblastoma cell-derived EVs reflects genome-wide tumor methylation and mutational profiles and allows noninvasive tumor subtype classification. DNA was isolated from EVs secreted by glioblastoma cells as well as from matching cultured cells and tumors. EV-DNA was localized and quantified by direct stochastic optical reconstruction microscopy. Methylation and copy number profiling was performed using 850k arrays. Mutations were identified by targeted gene panel sequencing. Proteins were differentially quantified by mass spectrometric proteomics. Genome-wide methylation profiling of glioblastoma-derived EVs correctly identified the methylation class of the parental cells and original tumors, including the MGMT promoter methylation status. Tumor-specific mutations and copy number variations (CNV) were detected in EV-DNA with high accuracy. Different EV isolation techniques did not affect the methylation profiling and CNV results. DNA was present inside EVs and on the EV surface. Proteome analysis did not allow specific tumor identification or classification but identified tumor-associated proteins that could potentially be useful for enriching tumor-derived circulating EVs from biofluids. This study provides proof of principle that EV-DNA reflects the genome-wide methylation, CNV, and mutational status of glioblastoma cells and enables their molecular classification.

BACKGROUND Corticotroph adenomas are immunopositive for adrenocorticotrophic hormone (ACTH) associated with elevated blood ACTH levels leading to Cushing disease (CD). Yet silent ACTH adenomas (SCA) immunostain for ACTH but do not cause hypercortisolism. SCA have consistently been shown to have a more aggressive postoperative course, then nonfunctioning and ACTH adenomas. Here we show that genome-wide methylation profiles can be used to distinguish ACTH adenomas from SCA. METHODS16 SCA patients and 19 CD patients that underwent transsphenoidal resection were included. Tumor size was measured by MRI. Tumor histology was proven by immunstain for ACTH and routine histopathology. Sanger sequencing was performed to analyse mutational burden within the ACTH locus (n=3). Genome-wide DNA methylation profiling was performed using a 850k Illumina EPIC array and classified by the DKFZ brain tumor classifier as well as PCA analysis using R (n=17). RESULTS Mean age was 55,4 and 46,2 for SCA and CD patients, respectively. Patients with SCA had significantly larger tumors (SCA: 12,56±3,07ccm; CD: 1,9±1,2ccm p< 0.01). Both SCA and CD tumors showed strong expression of ACTH as proven by immunohistochemistry. Sanger sequencing revealed no mutations within the ACTH gene locus in SCA. Both, SCA and CD tumors classified as ACTH adenomas by genome-wide methylation profiling. Further PCA of DNA methylation profiles allowed subtype tumor classification into SCA and CD adenomas. CONCLUSION SCA show a strong expression of ACTH without causing hypercortisolism. The reason for this is not yet known. Our data suggest that SCA do not harbor specific mutations within the ACTH gene locus. However genome wide methylation profiles allows subgrouping of SCA and CD adenomas.

Despite an established link between sleep deprivation and epigenetic processes in humans, it remains unclear to what extent sleep deprivation modulates DNA methylation. We performed a within-subject randomized blinded study with 16 healthy subjects to examine the effect of one night of total sleep deprivation (TSD) on the genome-wide methylation profile in blood compared with that in normal sleep. Genome-wide differences in methylation between both conditions were assessed by applying a paired regression model that corrected for monocyte subpopulations. In addition, the correlations between the methylation of genes detected to be modulated by TSD and gene expression were examined in a separate, publicly available cohort of 10 healthy male donors (E-GEOD-49065). Sleep deprivation significantly affected the DNA methylation profile both independently and in dependency of shifts in monocyte composition. Our study detected differential methylation of 269 probes. Notably, one CpG site was located 69 bp upstream of ING5, which has been shown to be differentially expressed after sleep deprivation. Gene set enrichment analysis detected the Notch and Wnt signaling pathways to be enriched among the differentially methylated genes. These results provide evidence that total acute sleep deprivation alters the methylation profile in healthy human subjects. This is, to our knowledge, the first study that systematically investigated the impact of total acute sleep deprivation on genome-wide DNA methylation profiles in blood and related the epigenomic findings to the expression data.

CpG Island Methylator Phenotype in Adrenocortical Carcinoma: Fact or Fiction?

Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide in 2020. Colonoscopy and the fecal immunochemical test (FIT) are commonly used as CRC screening tests, but both types of tests possess different limitations. Recently, liquid biopsy-based DNA methylation test has become a powerful tool for cancer screening, and the detection of abnormal DNA methylation in stool specimens is considered as an effective approach for CRC screening. The aim of this study was to develop a novel approach in biomarker selection based on integrating primary biomarkers from genome-wide methylation profiles and secondary biomarkers from CRC comorbidity analytics. A total of 125 differential methylated probes (DMPs) were identified as primary biomarkers from 352 genome-wide methylation profiles. Among them, 51 biomarkers, including 48 hypermethylated DMPs and 3 hypomethylated DMPs, were considered as suitable DMP candidates for CRC screening tests. After comparing with commercial kits, three genes (ADHFE1, SDC2, and PPP2R5C) were selected as candidate epigenetic biomarkers for CRC screening tests. Methylation levels of these three biomarkers were significantly higher for patients with CRC than normal subjects. The sensitivity and specificity of integrating methylated ADHFE1, SDC2, and PPP2R5C for CRC detection achieved 84.6% and 92.3%, respectively. Through an integrated approach using genome-wide DNA methylation profiles and electronic medical records, we could design a biomarker panel that allows for early and accurate noninvasive detection of CRC using stool samples.

To determine genome-wide DNA methylation profiles induced by Helicobacter pylori (H. pylori) infection and to identify methylation markers in H. pylori-induced gastric carcinogenesis. Gastric mucosae obtained from controls (n = 20) and patients with gastric cancer (n = 28) were included. A wide panel of CpG sites in cancer-related genes (1505 CpG sites in 807 genes) was analyzed using Illumina bead array technology. Validation of the results of Illumina bead array technique was performed using methylation-specific PCR method for four genes (MOS, DCC, CRK, and PTPN6). The Illumina bead array showed that a total of 359 CpG sites (269 genes) were identified as differentially methylated by H. pylori infection (p < .0001). The correlation between methylation-specific PCR and bead array analysis was significant (p < .0001, Spearman coefficient = 0.5054). Methylation profiles in noncancerous gastric mucosae of the patients with gastric cancer showed quite distinct patterns according to the presence or absence of the current H. pylori infection; however, 10 CpG sites were identified to be hypermethylated and three hypomethylated in association with the presence of gastric cancer regardless of H. pylori infection (p < .01). Genome-wide methylation profiles showed a number of genes differentially methylated by H. pylori infection. Methylation profiles in noncancerous gastric mucosae from the patients with gastric cancer can be affected by H. pylori-induced gastritis. Differentially methylated CpG sites in this study needs to be validated in a larger population using quantitative methylation-specific PCR method.

Genome-wide DNA methylation profiling after Ayurveda intervention to bronchial asthmatics identifies differential methylation in several transcription factors with immune process related function

Intracranial germ cell tumors (iGCTs) are the second most common brain tumors among children under 14 in Japan. The World Health Organization classification recognizes several subtypes of iGCTs, which are conventionally subclassified into pure germinoma or non-germinomatous GCTs. Recent exhaustive genomic studies showed that mutations of the genes involved in the MAPK and/or PI3K pathways are common in iGCTs; however, the mechanisms of how different subtypes develop, often as a mixed-GCT, are unknown. To elucidate the pathogenesis of iGCTs, we investigated 61 GCTs of various subtypes by genome-wide DNA methylation profiling. We showed that pure germinomas are characterized by global low DNA methylation, a unique epigenetic feature making them distinct from all other iGCTs subtypes. The patterns of methylation strongly resemble that of primordial germ cells (PGC) at the migration phase, possibly indicating the cell of origin for these tumors. Unlike PGC, however, hypomethylation extends to long interspersed nuclear element retrotransposons. Histologically and epigenetically distinct microdissected components of mixed-GCTs shared identical somatic mutations in the MAPK or PI3K pathways, indicating that they developed from a common ancestral cell.

Breast cancer is more common in European Americans (EAs) than in African Americans (AAs) but mortality from breast cancer is higher among AAs. While there are racial differences in DNA methylation and gene expression in breast tumors, little is known whether such racial differences exist in breast tissues of healthy women. Genome-wide DNA methylation and gene expression profiling was performed in histologically normal breast tissues of healthy women. Linear regression models were used to identify differentially-methylated CpG sites (CpGs) between EAs (n = 61) and AAs (n = 22). Correlations for methylation and expression were assessed. Biological functions of the differentially-methylated genes were assigned using the Ingenuity Pathway Analysis. Among 485 differentially-methylated CpGs by race, 203 were hypermethylated in EAs, and 282 were hypermethylated in AAs. Promoter-related differentially-methylated CpGs were more frequently hypermethylated in EAs (52%) than AAs (27%) while gene body and intergenic CpGs were more frequently hypermethylated in AAs. The differentially-methylated CpGs were enriched for cancer-associated genes with roles in cell death and survival, cellular development, and cell-to-cell signaling. In a separate analysis for correlation in EAs and AAs, different patterns of correlation were found between EAs and AAs. The correlated genes showed different biological networks between EAs and AAs; networks were connected by Ubiquitin C. To our knowledge, this is the first comprehensive genome-wide study to identify differences in methylation and gene expression between EAs and AAs in breast tissues from healthy women. These findings may provide further insights regarding the contribution of epigenetic differences to racial disparities in breast cancer.

BackgroundMethylation of CpG dinucleotides is a fundamental mechanism of epigenetic regulation in eukaryotic genomes. Development of methods for rapid genome wide methylation profiling will greatly facilitate both hypothesis and discovery driven research in the field of epigenetics. In this regard, a single molecule approach to methylation profiling offers several unique advantages that include elimination of chemical DNA modification steps and PCR amplification.ResultsA single molecule approach is presented for the discernment of methylation profiles, based on optical mapping. We report results from a series of pilot studies demonstrating the capabilities of optical mapping as a platform for methylation profiling of whole genomes. Optical mapping was used to discern the methylation profile from both an engineered and wild type Escherichia coli. Furthermore, the methylation status of selected loci within the genome of human embryonic stem cells was profiled using optical mapping.ConclusionThe optical mapping platform effectively detects DNA methylation patterns. Due to single molecule detection, optical mapping offers significant advantages over other technologies. This advantage stems from obviation of DNA modification steps, such as bisulfite treatment, and the ability of the platform to assay repeat dense regions within mammalian genomes inaccessible to techniques using array-hybridization technologies.

BackgroundAccurate classification of pediatric central nervous system (CNS) tumors is critical for optimal treatment yet remains challenging due to the limitations of traditional histopathological methods. DNA methylation profiling has gained attention as a promising tool for the molecular classification of CNS tumors. However, despite its potential clinical value, methylation classifiers remain limited to the research settings. This study aims to assess the use of two DNA methylation-based classifiers for CNS tumor diagnostics, with the eventual goal of integrating them into clinical practice and the impact of technical factors such as fixation methods, DNA quantity, and array choice, while exploring the utility of visualization tools (UMAP/t-SNE) and the integration of molecular data for resolving diagnostic ambiguities.MethodsWe analyzed 96 pediatric pathology tissue samples, including 75 CNS tumors, 10 with CNS non-tumoral lesions and 11 with non-CNS tumors, performing 130 methylation analyses. DNA from both formalin-fixed paraffin-embedded (FFPE) and fresh frozen (FF) tissues were used for methylation profiling using the Illumina MethylationEPIC V1 and V2 arrays. The performance of two DNA methylation-based classifiers (Heidelberg and NIH) was evaluated by comparing the classification results with histopathological diagnoses. Technical variables that may affect quality such as DNA quantity, extraction method, and sample fixation were also investigated.ResultsBoth classifiers demonstrated an 88% concordance with histopathological diagnoses in CNS tumors. Methylation profiling refined the histological diagnoses in 54.66% of cases and contributed to molecular subtyping in 52% of CNS tumor cases. The analysis in a small percentage of cases (5.33%) exhibited conflicting diagnoses, emphasizing the need for cautious interpretation and re-evaluation of the cases of uncertainty. Interestingly, both classifiers also identified CNS non-tumor tissues from tumor cases, although they misclassified some normal tissues and malformations as CNS tumors. Technical factors, including DNA quantity and sample fixation, had minimal impact on classifier performance.ConclusionThis study highlights the potential of DNA methylation profiling as a complementary diagnostic tool in pediatric CNS tumor classification, paving the way for its integration into routine clinical practice. To the best of our knowledge, this is the first publication comparing two DNA methylation classifiers in a pediatric CNS tumor cohort. While the classifiers show promise for improving diagnostic accuracy, especially in complex or undiagnosed cases, they should be used as a complementary tool to histopathologic classification. Further research is needed to validate their integration into clinical practice, including refining technical protocols, addressing limitations, and evaluating long-term clinical outcomes.

Background: Liquid biopsies analyzing cell-free DNA (cfDNA) from plasma and urine can offer clinical diagnostic information in a non-invasive way. One major application of DNA methylation in liquid biopsies focuses on targeted panel regions, aiming to detect hypermethylation in cancer related regions. Recent studies have revealed that the loss of DNA methylation is significantly enriched in late-replicating loci compared with early-replicating loci in cancer cells. In this study, we profiled genome-wide DNA methylation of plasma and urine samples from various of cancer indications to detect DNA methylation loss in tumor DNA. Methods: We used the PredicineEPIC assay for genome-wide DNA methylation profiling at 20× coverage. Unlike traditional differential methylation region (DMR) analysis, we identified fragment-level DNA methylation changes in CpG-rich genomic regions. For each fragment, we calculated the probability of the fragment being unmethylated versus methylated. This calculation is specific to genomic positions, with the background distribution built upon more than 100 healthy plasma samples. Results: Compared with genome-wide DNA methylation profile of healthy plasma samples, we observed two distinct types of methylation loss in cancer patient samples. In the first case, hypomethylation occurs at fragments with significant methylation loss, where the methylation status of all CpG sites transitions from methylated to unmethylated. We observed that the magnitude of methylation loss is positively correlated with tumor fraction. Based on wet-lab titration experiment results, we can achieve tumor fraction detection limit of 0.1% for prostate, breast, and lung cancers. In the second case, hypomethylation at the fragment level is more subtle, with only a portion of CpG sites becoming unmethylated. We found that many samples in this category had CHIP mutations or other methylation enzyme deficiencies detected, with DNMT3A, which encodes DNA methyltransferase, being the most frequent. The same method can also be applied to urine cfDNA data for the detection of bladder cancer. We analyzed over 500 whole-genome methylation samples across different cancer indications, including prostate, colon, pancreatic, lung, breast and bladder cancers. Overall, genome-wide DNA methylation loss is highly prevalent in cancer samples and varies across cancer types. Some chromosome regions are consistently hypo-methylated across samples and cancer indications. Conclusions: Our results indicate that global nuclear organization might be disrupted by methylation loss at closed chromatin compartments. The genome-wide methylation profile of circulating tumor DNA can measure cancer genome abnormality in terms of DNA methylation loss, CHIP mutations and copy number variations. Combination of all these features can provide a robust utility for cancer detection and estimating tumor fraction in liquid biopsies. Citation Format: Chao Dai, Giancarlo Bonora, Kemin Zhou, Shidong Jia, Pan Du. Genome-wide cfDNA hypo-methylation landscape of 500 patients, a real-world study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4557.

Biostimulants are obtained from various sources like plants, animals, microorganisms, and industrial by-products as well as waste material. Their utilization in agriculture practices is being increased that is giving positive results. The purpose of the current study was to use plant-derived smoke (SMK) solution and biogas digestate (BGD) slurry as biostimulant to elucidate their impact on potato (Solanum tuberosum) performance. The experiment was conducted in lab as well as field conditions, and SMK and BGD solutions were prepared in varying concentrations such as SMK 1:500, SMK 1:250, BGD 50:50, and BGD 75:25. Foliar applications were performed thrice during experiments and data were collected related to photosynthesis, growth, pigments, and genome-wide methylation profiling. Net photosynthesis rate (A) and water use efficiency (WUE) were found higher in SMK- and BGD-treated lab and field grown plants. Among pigments, BGD-treated plants depicted higher levels of Chl a and Chl b while SMK-treated plants showed higher carotenoid levels. Alongside, enhancement in growth-related parameters like leaf number and dry weight was also observed in both lab- and field-treated plants. Furthermore, DNA methylation profile of SMK- and BGD-treated plants depicted variation compared to control. DNA methylation events increased in all the treatments compared to control except for SMK 1:500. These results indicate that smoke and slurry both act as efficient biostimulants which result in better performance of plants. Biostimulants also affected the genome-wide DNA methylation profile that resultantly might have changed the plant gene expression profiling and played its role in plant responsiveness to these biostimulants. However, there is need to elucidate a possible synergistic effect of SMK and BGD on plant growth along with gene expression profiling.